102 research outputs found
Cross-Domain Car Detection Model with Integrated Convolutional Block Attention Mechanism
Car detection, particularly through camera vision, has become a major focus
in the field of computer vision and has gained widespread adoption. While
current car detection systems are capable of good detection, reliable detection
can still be challenging due to factors such as proximity between the car,
light intensity, and environmental visibility. To address these issues, we
propose a cross-domain car detection model that we apply to car recognition for
autonomous driving and other areas. Our model includes several novelties:
1)Building a complete cross-domain target detection framework. 2)Developing an
unpaired target domain picture generation module with an integrated
convolutional attention mechanism. 3)Adopting Generalized Intersection over
Union (GIOU) as the loss function of the target detection framework.
4)Designing an object detection model integrated with two-headed Convolutional
Block Attention Module(CBAM). 5)Utilizing an effective data enhancement method.
To evaluate the model's effectiveness, we performed a reduced will resolution
process on the data in the SSLAD dataset and used it as the benchmark dataset
for our task. Experimental results show that the performance of the
cross-domain car target detection model improves by 40% over the model without
our framework, and our improvements have a significant impact on cross-domain
car recognition
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